查看文章

Deep neural networks (DNNs) are capable of extracting effective features from data by using deep structure and multiple non-linear processing units. However, they dependent on large datasets from the same distribution. It is difficult to collect wafer maps with various defect patterns in semiconductor manufacturing process. A new deep transfer learning model, deep transfer Wasserstein adversarial network (DTWAN) is proposed to recognize wafer map defect. An adaptive transfer learning framework based on adversarial training is proposed for DTWAN, where multi-stage optimization based on the maximum mean discrepancy (MMD), cross entropy, and adversarial loss is performed. Finally, a generative adversarial algorithm is developed to guide the model to extract general features from source and target domain. DTWAN transfers the key knowledge of the source domain data to target domain, and effectively …